用边界注意力提升肾小球分割精度，改善相邻结构区分。
A deep learning framework for glomeruli segmentation with boundary attention
- 基于U-Net设计带边界注意力的解码器，强化关键区域识别。
- 在Dice和IoU指标上超越现有方法，显著提升分割精度。
- 适合病理图像分析、医学影像分割研究者参考使用。
肾小球的精确检测与分割对诊断至关重要。传统深度学习方法主要依赖语义分割，难以准确区分邻近肾小球。为此，我们提出一种新型肾小球检测与分割模型，注重边界分离。利用病理基础模型，所提出的基于U-Net的架构引入专用注意力解码器，突出关键区域，提升实例级分割性能。实验表明，该方法在Dice分数和交并比（IoU）上均优于当前最优方法，显示出更优的肾小球轮廓分割能力。
原文摘要 · Abstract (English)
Accurate detection and segmentation of glomeruli in kidney tissue are essential for diagnostic applications. Traditional deep learning methods primarily rely on semantic segmentation, which often fails to precisely delineate adjacent glomeruli. To address this challenge, we propose a novel glomerulus detection and segmentation model that emphasises boundary separation. Leveraging pathology foundation models, the proposed U-Net-based architecture incorporates a specialised attention decoder designed to highlight critical regions and improve instancelevel segmentation. Experimental evaluations demonstrate that our approach surpasses state-of-the-art methods in both Dice score and Intersection over Union, indicating superior performance in glomerular delineation.
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